Instructions to use Javiai/3dprintfails-yolo5vs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Javiai/3dprintfails-yolo5vs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Javiai/3dprintfails-yolo5vs")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Javiai/3dprintfails-yolo5vs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68887ebbf5d0923c1ee6a8d2767e7b747d2ae90b3c27c48e3b125be13d776d9b
- Size of remote file:
- 14.4 MB
- SHA256:
- 353d02476368025fef6a76dc6f4cd02a5eb84962170d3636ea2fb5c4f3386cbd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.